Finding Your Totem: A Digital Intervention to Promote Strengths-Oriented Feedback Within Work Teams
Bibliographic record
Abstract
Workplace strengths interventions have been linked to improved worker well-being and performance, but their accessibility and sustainability are often questioned. This research examined the impact of an online strengths-based activity, the Totem activity, on need satisfaction, autonomous motivation, psychological well-being, and perceived team effectiveness. Using a mixed-methods approach combining quasi-experimental and longitudinal design features, we studied the outcomes of this activity on a sample of full-time workers (n = 395) and contrasted them to those of a control group (n = 61). Data was gathered pre- and post-intervention for both groups, with the experimental group providing additional feedback three weeks post-intervention. The findings revealed that the experimental group displayed notable increases in all of the outcome variables post-intervention compared to the control group, with effect sizes varying from low to medium. Longitudinal analyses via a latent change score model (LCSM) indicated that changes in need satisfaction post-intervention were predictive of shifts in work motivation, psychological well-being, and team effectiveness over three weeks. Additionally, autonomous motivation appears to partially mediate the link between changes in need satisfaction pre- and post-activity and the shifts in perceived team effectiveness over three weeks. However, variations in need satisfaction consistently emerged as the sole significant predictor of well-being during the same period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".